Daniel Burm (Xebia): ‘Re-imagine business processes with AI-first thinking’
Companies that rely only on traditional process improvement tools are missing the bigger opportunity of the AI era, according to Daniel Burm, management consultant at Xebia.
When it comes to making use of AI in process redesign, Burm argues that classic techniques, such as value stream mapping (VSM), process mining, and event storming, are still useful for understanding how a process works today, but they were never built to reveal where AI could change that process altogether.
Mapping the current state
VSM can provide the foundation for spotting where a current process breaks down, though he says it does not point directly to where AI can have an impact. “VSM gives me exactly the foundation I need to clearly identify and understand where the current process can be improved, but not directly where AI can have an impact,” said Burm.
“Standard VSM mostly remains focused on optimizing the existing process. For an AI-native redesign, you need to take it one step further.”
To close that gap, he recommends adding a few questions to any standard mapping exercise, covering what data is being used, by whom, and how good that data actually is, along with which steps could realistically be handed to an AI agent or augmented by one.
A different way to think about process
According to Burm, the bigger shift is not technical but conceptual. He suggests that companies stop viewing a process as a sequence of human activities to be trimmed and instead see it as a collection of connected, reusable AI capabilities.
“The process will become more of a collection of connected, potentially reusable AI capabilities than of activities,” Burm explained.
“If you look at it this way, it’s easier to think AI native, and it also changes the way you think about how to judge if the process is actually performing. Just think about it, does the touch time of an activity actually matter when it’s performed by an agent? A more telling signal might be token cost per output.”
Big-bang versus iterative redesign
When it comes to actually making the change, Burm lays out two options and is candid about the trade-offs of each. “A big-bang redesign can be powerful when the current process is clearly broken and hardly functional, when there is strong executive sponsorship, and when the organization is ready to accept a more radical change,” said Burm.
“It creates momentum, and it forces people to look beyond the limitations of the existing way of working. The downside is that it can also become too conceptual too quickly. People may understand the ambition but not yet see its impact on their work.”
For that reason, Burm said he generally favors an iterative approach.
“It allows you to start with a concrete part of the process, learn from it, test the new AI capabilities in real use, and gradually build trust,” he said.
“This is especially important because AI-native processes are not just process changes. They also change decision rights, roles, responsibilities, quality controls and sometimes even the customer experience itself.”
People still matter, just differently
Burm stresses that redesigning a process around AI is not purely a technology project. On what happens to people in these redesigned processes, he described a shift rather than a disappearance.
“In many AI-native processes, the human role does not disappear. It shifts,” said Burm.
“Humans move from doing every step themselves to setting direction, checking exceptions, judging quality, handling ambiguity and taking responsibility for decisions that matter. This is where risk management becomes part of the design, not a control afterward.”
Where companies should begin
In terms of how companies can get started in approaching AI differently, Burm’s advice is to resist the instinct to tackle the most critical or highest-visibility process first.
“I would not always start with the most exposed or most critical process,” he said. “Start with something meaningful enough to matter, but safe enough to learn. You want a process with real pain, available, decent-quality data, and enough room to experiment.”
Underlying all of this, Burm argues, is the need for a mix of skills that is rarely found in one team. “If you only bring technology expertise, you risk building something that does not fit the organization,” he said. “If you bring only process expertise, you risk optimizing yesterday’s process by 10%. The value is in putting both on the table.”
